Driver assistance systems support the driver in his driving task and are thus helping to make road traffic safer in future and to reduce accident figures. Camera-based driver assistance systems hereby detect the surroundings of a vehicle. Camera systems which are located behind the windshield detect the area in front of the vehicle according to the driver's visual perception. The functional scopes of such assistance systems extend from intelligent headlamp control to detecting and displaying speed limits to warnings in the event of the vehicle failing to keep to its lane or an impending collision. In addition to camera systems, radar sensors, lidar sensors and/or laser scanners help to detect other vehicles, unprotected road users such as e.g. pedestrians and cyclists and the infrastructure such as e.g. crash barriers and traffic lights. This is therefore creating the requirement to depict the immediate vehicle environment more and more accurately.
The degree of automation of motor vehicles will continue to rise in future. As a result, the level of equipping of vehicles with sensor technology will also increase very significantly. Highly or fully automated vehicles will be equipped with a plurality of different sensors and will thus allow a 360° view, in particular via camera systems.
An essential part of a driver's driving task is correctly assessing the roadway state and, thus, the available friction coefficient between tires and the roadway, in order to then adapt his driving style accordingly. In future, highly and fully automated vehicles will take over the task of driving at least in partial areas. To this end, it is essential for the roadway state to be correctly detected and assessed by the system.
DE 10 2004 018 088 A1 shows a roadway detection system having a temperature sensor, an ultrasonic sensor and a camera. The temperature, roughness and image data (roadway data) obtained from the sensors is filtered and compared with reference data, and a level of security is generated for the comparison. The state of the roadway surface is detected on the basis of the comparison of the filtered roadway data with the reference data. The roadway surface (e.g. concrete, asphalt, dirt, grass, sand or gravel) and the state thereof (e.g. dry, icy, snowy, wet) can be classified in this way.
WO 2012/110030 A2 shows a method and a device for estimating coefficients of friction using a 3D camera, e.g. a stereo camera. At least one image of the environment of the vehicle is recorded by means of the 3D camera. A height profile of the road surface is created in the entire area in front of the vehicle from the image data of the 3D camera. The anticipated local coefficient of friction of the road surface in the area in front of the vehicle is estimated from the height profile. In individual cases, the roadway surface can be classified, e.g. as a blanket of snow or a muddy dirt track, from special detected height profiles.
WO 2013/117186 A1 shows a method and a device for detecting the condition of a roadway surface by means of a 3D camera. By means of the 3D camera, at least one image of the surroundings extending in front of the vehicle is acquired. Height profiles of the roadway surface which extend transversely to the direction of motion of the vehicle are determined from the image data of the 3D camera along a plurality of lines. The condition of the roadway surface is detected from the determined height profiles. In addition to the determined height profiles, 2D image data from at least one monocular camera of the 3D camera is optionally evaluated, e.g. by means of a texture or pattern analysis, and is incorporated into the detection of the condition of the roadway surface.
However, the known methods place high demands on the required sensor technology. Therefore, in the case of the indicated methods and/or devices, either a temperature and ultrasonic sensor are required in addition to a camera, or the camera must be configured as a 3D sensor, so that the classification results are sufficiently robust.